Abstract
Fake news has spread more widely over the past few years. The development of social media and internet websites has fueled the spread of fake news, causing it to mix with accurate information. The majority of studies on Fake News Detection FND were in English, but recent attention has been focused on Arabic. However, there aren't many studies on Arabic fake news detection. In this work, a newArabic fake news detection approachhas been proposedusing Arabic dataset publically availableand a translated English fake news dataset intoArabic. A new model Text-CNNs based on 1D Convolution Neural Networks CNNs has been used for classification real and fake news. Extensive experimental results on the Arabic fake news dataset show that our proposed approach provided high detection accuracy about (99.67%), Precision (99.45), Recall (99.65) and F1-score (99.50).
Recommended Citation
Shaker, Khalid and Alqudsi, Arwa
(2024)
"Approach for Detecting Arabic Fake News using Deep Learning,"
Iraqi Journal for Computer Science and Mathematics: Vol. 5:
Iss.
3, Article 1.
DOI: https://doi.org/10.52866/ijcsm.2024.05.03.049
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol5/iss3/1
